In traffic management and law enforcement, it is frequently necessary to identify individual vehicles using image captures from video and still frame cameras. Because license plate numbers, when combined with relevant issuing authorities, such as states or government agencies, are typically unique, license plates are a good way to uniquely identify vehicles from captured images. The process of automatically determining license plate information, such as license plate numbers, from captured images of license plates is known as automatic license plate recognition (ALPR).
Traditionally, ALPR has relied on optical character recognition (OCR) technology to determine license plate information from digital images. OCR typically operates by isolating individual characters in an image according to color and contrast differentials and then comparing individual scanned character shapes to pre-stored shapes or dimension-based algorithms associated with particular American Standard Code for Information Interchange (ASCII) or Unicode characters. Thus, the accuracy of an OCR process often depends greatly on the resolution and quality of the image on which it operates. In some implementations, if an OCR system is not able to match a particular scanned character to a known character with sufficient confidence, the OCR system may further make assumptions about the scanned character based on letter patterns associated with particular spelling or grammar rules.
Unfortunately, for ALPR systems, OCR presents a number of significant drawbacks. Often, images that are captured of license plates have numerous defects that may make it difficult for OCR to accurately decipher characters. Such defects may be caused by differing levels of lighting on a license plate due to differing times of day or weather conditions; blur resulting from fast moving vehicles; rust, deformation, or other physical defects in license plates; reduced contrast due to image captures based on infrared light; etc. Moreover, unlike character sequences representing words or phrases, license plate character sequences are typically random, such that OCR systems may not be able to increase certainty in deciphering characters based on their relationships to adjacent characters. And, while a certain degree of error may be tolerable for some OCR applications, such as an occasionally misspelled word resulting from an OCR processing of a document, even a single incorrect character may render an OCR result useless in the ALPR context, in which a vehicle is often uniquely identifiable by only a single license plate character sequence.
Given these shortcomings in using traditional OCR processes in ALPR, recent attention has been given to the idea of using image signature-based matching techniques to identify vehicle license plates. Signature-based image matching differs from OCR in several respects. Most importantly, whereas OCR operates by attempting to isolate and interpret each individual character within a larger image, signature-based image matching works by considering graphical characteristics of the image as a whole and distilling those characteristics into a more succinct signature, often represented as a vector. Two images may then be compared by evaluating the similarity of their signatures, for example using a vector dot product computation, even if individual characters cannot be discerned from one or both images using conventional OCR techniques.
Signature matching thus approximates image matching by representing portions or characteristics of images as compact binary or alphanumeric strings, which may be more easily compared than large numbers of pixels and also more distinct than image pixels. If image signatures are further represented as vectors, the dot product may allow a measure of similarity between two signature strings to be computed, even if they do not match completely. However, just as not all portions or characteristics of an image may be equally important when comparing two images for similarity, not all elements of a given signature, which elements represent portions or characteristics of the images, may be equally important when comparing two images using signature matching. Therefore, one enhancement to signature matching is to employ signatures that have differently weighted elements.
In the context of ALPR, for instance, it may be more important to compare certain portions or characteristics of license plates—e.g., license plate number, issuing authority, design, font type, etc.—than other portions or characteristics—e.g., empty “whitespace,” license plate frame, etc. Thus, to improve ALPR signature matching, elements within signatures of license plate images may be weighted to emphasize the portions or characteristics that are more probative of similarity than other portions or characteristics. However, it may be difficult to determine and program an optimal set of weights for a given license plate, given the wide variety of fonts, backgrounds, and other characteristics of license plates across different issuing authorities and designs.
Accordingly, there is a need for methods and systems for determining one or more optimal sets of weights for use in weighted image signature matching. Such a need is particularly acute in the area of ALPR.